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Optimal Excitation Signal Design for Frequency Domain System Identification Using Semidefinite Programming
Optimal Excitation Signal Design Frequency Domain System Identification Semidefinite Programming
2015/7/13
The paper discusses two methods of optimal excitation signal design for identification with Maximum Likelihood parameter estimation: The ‘classical’, dispersion function based method, and a new, semid...
Semidefinite Programming
semidefinite programming convex optimization interior-point methods eigenvalue optimization
2015/7/13
In semidefinite programming we minimize a linear function subject to the constraint that an affine combination of symmetric matrices is positive semidefinite. Such a constraint is nonlinear and nonsmo...
Semidefinite Programming Relaxations of Non-Convex Problems in Control and Combinatorial Optimization
Semidefinite Programming Relaxations Non-Convex Problems Control Combinatorial Optimization
2015/7/13
We point out some connections between applications of semidefinite programming in control and in combinatorial optimization. In both fields semidefinite programs arise as convex relaxations of NP-hard...
Connections Between Semi-Infinite and Semidefinite Programming
Connections Between Semi-Infinite Semidefinite Programming
2015/7/10
Some interesting semi-infinite optimization problems can be reduced to semidefinite optimization problems, and hence solved efficiently using recent interior-point methods. In this paper we discuss se...
A wide variety of nonlinear convex optimization problems can be cast as problems involving linear matrix inequalities (LMIs), and hence efficiently solved using recently developed interior-point metho...
Designing Fast Distributed Iterations via Semidefinite Programming
Designing Fas Distributed Iterations Semidefinite Programming
2015/7/10
The general setting we consider involves a process, iteration, or method in which the computation or communication at each step is local, determined by a given graph, and involves some parameters or c...
Generalized Chebyshev Bounds via Semidefinite Programming
semidefi nite programming convex optimization duality theory Chebyshev inequalities
2015/7/10
A sharp lower bound on the probability of a set defined by quadratic inequalities, given the first two moments of the distribution, can be efficiently computed using convex optimization. This result g...
Further Relaxations of the Semidefinite Programming Approach to Sensor Network Localization
sensor network localization semidefi nite programming
2015/7/9
Recently, a semidefinite programming (SDP) relaxation approach has been proposed to solve the sensor network localization problem. Although it achieves high accuracy in estimating the sensor locations...
Sparse Non Gaussian Component Analysis by Semidefinite Programming
dimension reduction non-Gaussian components analysis feature extraction
2011/7/5
Sparse non-Gaussian component analysis (SNGCA) is an unsupervised method of extracting a linear structure from a high dimensional data based on estimating a low-dimensional non-Gaussian data component...
The performance of principal component analysis (PCA) suffers badly in the presence of outliers. This paper proposes two novel approaches for robust PCA based on semidefinite programming.